Fuzzy immune particle swarm optimization algorithm and its application in scheduling of MVB periodic information

• Published in: Journal of intelligent & fuzzy systems Vol. 32; no. 6; pp. 3797 - 3807
• Main Authors: Wang, Yizhao, Wang, Lide, Yan, Xiang, Shen, Ping
• Format: Journal Article
• Language: English
• Published: London Sage Publications Ltd 01.01.2017
• Subjects: Algorithms; Co-design; Computer simulation; Convergence; Evolutionary algorithms; Fitness; FPSO; Fuel consumption; Fuzzy control; Fuzzy logic; Fuzzy systems; Mathematical analysis; Particle swarm optimization; Scheduling
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/IFS-152067

This paper addresses the scheduling problem of periodic information in multifunction-vehicle-bus networked control system (MNCS). To deal with this issue, a novel fuzzy immune particle swarm optimization (FIPSO) algorithm is proposed to eliminate the premature convergence of standard PSO algorithms in solving complex problem, which uses the fuzzy particle swarm optimization (FPSO) algorithm and the immune particle swarm optimization (IPSO) algorithm for reference. Through designing the fuzzy logic controller (FLC), the inertia weight and the immune-execute factor are regulated dynamically on the basis of the evolutionary time, the variation of average fitness value and the population diversity. The FLC also guides evolutionary directions and decides whether to execute immune operations, making the algorithm converge for many times. The simulation and calculation results of the scheduling example show that FIPSO algorithm has strong global search ability, good stability and better optimization performance. Particularly, based on the variable individual periods, the co-design of scheduling and control in MNCS is implemented by applying the FIPSO algorithm.